Applied Longitudinal Data Analysis

This course provides an overview of Longitudinal Data Analysis. As well as the statistical theory, an overview of the many applications and capabilities of LDA is given.

This course is designed as an introductory course for applied researchers and as such, is suitable for participants who want to develop a fundamental knowledge of LDA techniques.

Level 3 - runs over 5 days

Dr Mark Griffin is the Director of Insight Social Research & Statistics ( Insight focuses on research methodologies (including survey design and statistics) for public health, monitoring and evaluation for government and non-government organizations, and academic research. Insight has a secondary interest in providing IT services (as a Microsoft Business Partner). 

Insight is based at the Gold Coast Health and Knowledge Precinct. The Precinct contains Griffith University Gold Coast, the Gold Coast University Hospital, the Gold Coast Private Hospital, and the Cohort and Lumina tech parks. Insight provides research, consulting, training, and IT support services for clients across the Precinct and for the broader international community.

To date he has presented over 100 two-day and 40 five-day workshops in statistics around Australia

Course dates: Monday 15 January 2018 - Friday 19 January 2018
Course status: Course completed (no new applicants)
Week 1
About this course: 

Longitudinal Data Analysis is a very popular statistical method in a range of fields including medicine, natural resource management, business and economics.


As well as allowing a researcher to elicit the changes in a subject (person, business, etc) over time, longitudinal data is also exceedingly powerful as it allows the within-subject and between-subject variance to be estimated independently (generally allowing the parameters in the statistical model to be estimated with a much tighter accuracy than traditional models).


This course provides an overview of Longitudinal Data Analysis. As well as the statistical theory, an overview of the many applications and capabilities of LDA is given. The course is not particularly mathematical, but instead places emphasis on the fundamental concepts of LDA and how it is used by applied researchers.


The workshop will focus on linear mixed effects models, however we will also go through generalized linear mixed effects models and any new material a participant will need to understand in order to successfully use generalized LME’s. There will be exercises using both types of models.

General aims of the course are for students to develop a readiness for using LDA software and to develop the requisite knowledge for applying LDA methods and models in an intelligent way. Note that participants may be invited to briefly present their own research on the last day of class. This exercise, along with the formal lecture material, might help participants to chart a direction forward in their study and application of LDA.


This course has also been developed in consultation with staff from the National Centre for Longitudinal Data, Dept of Social Services. As such students will have access to a number of datasets through this workshop including one or more of:

  • The Housing, Income and Labour Dynamics in Australia (HILDA) survey
  • The Longitudinal Study of Australian Children (LSAC)
  • The Longitudinal Study of Indigenous Children (LSIC)
  • Building a New Life in Australia (BNLA) – a longitudinal study of humanitarian migrants

These studies have followed around 10,000 participants for approximately 10 years (further details, including precise study characteristics, can be found at It is also expected that this workshop will include a guest presentation from a DSS representative.

Course syllabus: 

Day 1

  • Revision of linear, logistic and Poisson regression


Day 2 and Day 3

  • Mixed effects models
  • Fixed and random effects
  • Random intercept and random slope
  • Goodness of fit measures (including Likelihood and AIC)
  • Model choice
  • Having more than one clustering level


Day 4

  • Survival or time-to-event analysis
  • Kaplan-Meier curves
  • Survival and hazard functions


Day 5

  • Missing data
  • Multiple imputation
  • Heckmann Selection models
Course format: 

This course will take place in a computer lab unless otherwise notified.


Approximately half of the time during this course will be spent in PowerPoint presentations, and half of the time in computer demonstrations and self-paced computer exercises (conducted in Stata).


Recommended Background: 

Participants must have completed the course Fundamentals of Multiple Regression or an equivalent course at university level and/or have equivalent experience.

Familiarity with analysis of variance, factor analysis or regression is desirable, but not strictly necessary. It is assumed that participants have little or no familiarity of structural equations with latent variables.

Recommended Texts: 

The instructor's bound, book length course notes will serve as the course texts.


Other references include:

  • Singer J.D., Willett J.B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
  • Hedeker D., Gibbons R.D. (2006). Longitudinal Data Analysis
  • Diggle P., Heagerty P. (2013). Analysis of Longitudinal Data
Course fees
Non Member: 
Full time student Member: 

The instructor's bound, book length course notes will serve as the course texts.

Supported by: 

Stata is distributed in Australia and New Zealand by Survey Design and Analysis Services.